1. Field of the Invention
The present invention relates to an image processing apparatus and an object detecting method.
2. Description of the Related Art
An image processing method of automatically detecting a specific object pattern from an image is very useful and can be used, for example, for a discrimination of a human face. Such a method can be used in many fields such as communication conference, man-machine interface, security, monitor system for tracing the human face, image compression, and the like. As such a technique for detecting the face from the image, various kinds of systems have been mentioned in, for example, Yang et al., “Detecting Faces in Images: A Survey”, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol. 24, No. 1, January 2002. Among them, a system for detecting the human face by using some typical features (two eyes, mouse, nose, etc.) and peculiar geometrical positional relations among those features is shown.
For example, the system proposed in Rowley et al., “Neural network-based face detection”, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol. 20, No. 1, January 1998 is a method of detecting a face pattern in an image by a neural network. The face detecting method disclosed in such a literature will be simply described hereinbelow.
First, an apparatus reads image data in which the face is detected as a target into a memory and extracts a predetermined area to be collated with the face from the read image. The apparatus inputs distribution of pixel values of the extracted area and obtains one output by an arithmetic operation by the neural network. At this time, a weight and a threshold value of the neural network have previously been learned by a very large number of face image patterns and non-face image patterns. For example, when an output of the neural network is equal to or larger than 0, the apparatus determines the object as a face, and when the output is a value other than it, the apparatus decides the object as a non-face. In the apparatus, an extracting position of the image pattern which is collated with the face as an input of the neural network is scanned, for example, sequentially in the vertical and lateral directions from the whole image area, thereby detecting the face from the image. In order to cope with the detection of the faces of various sizes, the apparatus sequentially reduces the read image at a predetermined rate and performs the scan for the face detection mentioned above with respect to the reduced images.
In the case of applying the above face detection to a surveillance camera, it is demanded to preferentially detect a photographing object having a possibility that it will disappear from a display screen between frames. That is, this is because in the object in which a possibility that it will disappear from the display screen is high, if the object cannot be detected in a certain frame, since a possibility that it will disappear from the display screen is high, the object cannot be detected either in the next and subsequent frames.
The object having the possibility that it will disappear from the display screen is an object whose motion in the display screen is large. In other words, it is necessary to detect from the object whose motion in the display screen is large between the frames.
Whether the motion in the display screen is large or small depends on a distance between the object and the camera. For example, in FIG. 1, there are an object A and an object B. Even if the object A and the object B moved by the same amount, the motion of the object A is large because it is close to the camera. On the contrary, the motion of the object B is small because it is far from the camera. FIG. 1 is a diagram for describing relations between the camera and the objects to be photographed.
Subsequently, the object A which is close to the camera is displayed large. On the contrary, the object B which is far from the camera is displayed small.
In the surveillance camera, therefore, it is necessary that the object which was displayed large is detected preferentially to the object which was displayed small.
However, in the related art, since the face detecting process is necessary for images of various kinds of resolution, a calculation amount is large. For example, there is such a problem that in the case where the face detection was performed on the image of a large size, if the calculation is interrupted on the halfway, the face displayed large on the display screen cannot be detected. In the case where the face detection was performed on the image of a small size, if the calculation is interrupted on the halfway, the face displayed small on the display screen cannot be detected.